Some previous works have incorporated the dependency structure information of sentences in their neural models for relation extraction. [1]} used a long short-term memory network (LSTM) [2]} along the shortest dependency path (SDP) between two entities to find the relation between them. Each token along the SDP is represented using four embeddings – pre-trained word vector, POS tag embedding, embedding for the dependency relation between the token and its child in the SDP, and embedding for its WordNet [3]} hypernym. They divide the SDP into two sub-paths: (i) The left SDP which goes from entity 1 to the common ancestor node (ii) The right SDP which goes from entity 2 to the common ancestor node. This common ancestor node is the lowest common ancestor between the two entities in the dependency tree. The token vectors along the left SDP and right SDP are passed to an LSTM layer separately. A pooling layer is applied to the hidden vectors to extract the feature vector from the left SDP and right SDP. These two vectors are concatenated and passed to a classifier to find the relation.
